Neil,
pls tell why do you need the correlation matrix? if you are trying to
simulate correlated variables then you can go around the cholesky by using
svd.
if you really need the correlation ( I think it is always possible to avoid
it ) then Rmetrics have a function to turn your matrix into positive
semi-definite.
you can also consider a factor model which will reduce the dimensionality
tremendously.
Stephen C. Bond
On Apr 1, 2009, ngottl...@marinercapital.com wrote:
Dear fellow R Users:
I am doing a Cholesky decomposition on a correlation matrix and get error
message
the matrix is not semi-definite.
Does anyone know:
1- a work around to this issue?
2- Is there any approach to try and figure out what vector might be
co-linear with another in thr Matrix?
3- any way to perturb the data to work around this?
Thanks for any suggestions.
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